Here, \(\mathcal {G}_{\theta }\)  and \(\mathcal {G}_{\phi }\)  are CNN generators whose weights are denoted by \(\theta \)  and \(\phi \) , respectively. Here, \(\mathbf {Z} = \left[\mathbf {z}_1,..,\mathbf {z}_N\right]\)  are time-dependent latent vectors that are also learned from the measured k-space data. Here, \(\mathbf {U}_0\)  is an initial approximate reconstruction that is often obtained using a simple algorithm, which is refined by the generator. See Fig. REF  for details. We observe that feeding an initial reconstruction of the \(\mathbf {U}\)  factors to \(G_{\theta }\)  offers faster reconstructions compared to the DIP strategy of feeding noise to the image generator. Similar to DIP [1]}, we expect to capitalize on the structural bias of the CNNs towards smooth natural images.
